Urinary tract infections (UTIs) represent the largest single type of healthcare-associated infections. The excess morbidity and costs attributable to healthcare-associated UTIs may now be increasing due to the rising prevalence of multi-drug resistant uropathogens. While surveillance and epidemiologic studies of these infections have been widespread, they have largely been limited to those infections that are identified during hospitalization. Yet the risk of acquiring a healthcare-associated infection does not end immediately upon discharge from the hospital. Thus, our long-term goal is to estimate the burden of community-onset, healthcare-associated UTI in our patient population and to utilize epidemiologic data to develop interventions aimed at reducing the incidence of healthcare-associated UTIs. The objective of the research proposed in this application is to collect preliminary data that can guide future epidemiologic studies of community-onset, healthcare-associated UTIs. To accomplish our objective, we propose the following aims: (1) estimate the incidence of community-onset, hospital- acquired urinary tract infections diagnosed among patients hospitalized in the family medicine service at Oregon Health & Science University (OHSU), and (2) identify potential risk factors for the development of community-onset, healthcare-associated urinary tract infections. This research will be set within the OHSU Family Medicine patient population, which has practice agreements in place to establish continuity of care for patients across inpatient and outpatient healthcare encounters. To accomplish Aim 1, we will quantify new diagnoses of community-onset, healthcare-associated UTI among at-risk patients. To accomplish Aim 2, we will conduct a pilot study using the matched case-control design. The proposed pilot study will result in an estimate of the incidence of community-onset, healthcare-associated UTIs in our patient population and provide the preliminary data needed to design a larger, well-powered epidemiologic study to identify the risk factors for these infections. We anticipate that these data will in turn facilitate the development of interventions to reduce the incidence of these infections by identifying those patients at the highest risk for community-onset, healthcare-associated UTIs.